Maintaining Review Credibility Using NLP, Reputation, and Blockchain

Abstract

This paper presents a novel approach to review credibility in a marketplace, which leverages trust in reviews and reputation of the parties who provide them. We propose an architecture for a reputation-based review evaluation system, which is built on top of the blockchain system, in order to ensure correct and trustworthy assessments. In our proposal, trustworthiness of reviews is evaluated using NLP—specifically, sentimental analysis—and the reviewers’ reputations are adjusted according to this evaluation. These reputations are stored on the blockchain and used as an asset for the consensus process when mapped to stake. We introduce a new type of transaction, a review transaction, which stores the review data and evaluation results. In testing, our simulation results showed that the NLP component incurred a reasonable delay this new type of review transactions. Additionally, we measured the time required to add a standard payment transaction in Algorand and that for our review transaction and observed comparable results. Also, we observed that the NLP component ensures an accurate credible evaluation (compared to the ground truth) of the product review texts. With this new model, we have moved towards showing how NLP can used for self-regulating trust management in a decentralized marketplace ecosystem.